The present invention relates to the field of item and peer recommendation algorithms directed towards industry professionals. More specifically, the present invention relates to methods and apparatus for extracting relevant content from email messages and email threads and using the extracted content in a system for recommending items and/or experts. The extracted content can be used to augment a user's implicit profile or stored in the recommendation engine database as question and/or answer content relating to a particular topic or topics, which database is accessible by the recommendation engine in response to future queries.
Document and expert recommendation systems are well-known. Most such systems employ a profile database which stores an explicit profile of each of the users of the system. An explicit profile may include information that generally defines the user based on the user's direct input into the system. Such information is usually derived from registration forms where the user has input his industry experience, job titles and duty descriptions, size of company, company name, projects he is working on, vendors he is working with, etc. Recommendation systems (also referred to herein as recommendation engines) of the type developed by Gartner, Inc. and Senexx, Inc. use not only an explicit profile of the user but also an implicit profile of the user, which is derived from a user's behavior, for example from tracking a user's actions on one or more electronic devices and/or on the web site used to access the recommendation engine. An example of an implicit profile is discussed in U.S. application Ser. No. 14/533,398 entitled Implicit Profile for Use With Recommendation Engine and/or Question Router, owned by Gartner, Inc., which is incorporated herein and made a part hereof for all purposes.
A business user's most common mode of communication is email. Hence email often encompasses the full spectrum of a typical business user's activities and provides a current and frequent reflection of these activities. Consequentially, email messages contain valuable information relating to the author's skills, background, current interests, specialties, and current activities.
Thus, information derived from emails may be quite valuable in keeping the implicit profile of the user (sender or email author) current and complete.
In addition, email is often used to share information and to answer questions, hence generating “content” that may be stored and later used by a recommendation engine.
However, the email “content” is typically shared only with the recipients of the email, may need to be stringed together from multiple emails written by multiple authors, and is only stored on the email server or the user's computer or electronic device. Thus, email content, in its current form, allows little leverage beyond the related email thread.
It would be advantageous to extract implicit profile attributes and/or question and answer content that can be stored in a database and fed into, e.g., a recommendation engine for use when a similar question arises in a forum.
The methods and apparatus of the present invention provide the foregoing and other advantages.